Skip To Main Content
Anastasia Krithara on Muck Rack

Anastasia Krithara

As seen in: Nature, MDPI, BioRxiv
Researcher at NCSR Demokritos. Interested in Machine Learning, text mining, information retrieval
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with Anastasia

Contact Anastasia, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

Mapping the AI life sciences landscape in Greece: a bibliometric comparison with global patterns

|
By Eleni Adamidi, Alexandros C. Dimopoulos, Anastasia Krithara, Anastasios Nentidis, Nikos Pechlivanis, Fotis E. Psomopoulos, Thanasis Vergoulis, Serafeim Chatzopoulos, Kleanthis Vichos
| Nature Verified
Abstract Artificial intelligence is increasingly used in Life Sciences, though the pace and direction of adoption varies widely across countries. To map the Greek landscape, we performed a data‑driven analysis of 916,824 AI-related life-science papers harvested from OpenAlex and PubMed. We tagged each publication with Medical Subject Headings (MeSH) and compared topic frequencies between articles linked to at least one Greek institution and the rest of the world.
Open in Who Shared Issue with article?

Machine Learning Methods for Classifying Multiple Sclerosis and Alzheimer’s Disease Using Genomic Data

|
By Magdalena Arnal Segura, Giorgio Bini, Anastasia Krithara, Georgios Paliouras
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Open in Who Shared Issue with article?

Machine learning methods applied to classify complex diseases using genomic data

|
By Magdalena Arnal Segura, Anastasia Krithara, Giorgio Bini, George Paliouras
| BioRxiv
Abstract Complex diseases pose challenges in disease prediction due to their multifactorial and polygenic nature. In this work, we explored the prediction of two complex diseases, multiple sclerosis (MS) and Alzheimer's disease (AD), using machine learning (ML) methods and genomic data from UK Biobank.
Open in Who Shared Issue with article?
See all 5 articles